SoroushVahidi/scidocs huggingface dataset:
Fast ε-free Inference of Simulation Models with Bayesian Conditional Density Estimation
(2016)

Many statistical models can be simulated forwards but have intractable likelihoods. Approximate Bayesian Computation (ABC) methods are used to infer properties of these models from data. Traditionally these methods approximate the posterior over parameters by conditioning on data being inside an -ball around the observed data, which is only correct in the limit →0. Monte Carlo methods can then draw samples from the approximate posterior to approximate predictions or error bars on parameters. These algorithms critically slow down as →0, and in practice draw samples from a broader distribution than the posterior. We propose a new approach to likelihood-free inference based on Bayesian conditional density estimation. Preliminary inferences based on limited simulation data are used to guide later simulations. In some cases, learning an accurate parametric representation of the entire true posterior distribution requires fewer model simulations than Monte Carlo ABC methods need to produce a single sample from an approximate posterior.
Gradient Descent Quantizes ReLU Network Features
(2018)

Deep neural networks are often trained in the over-parametrized regime (i.e. with far more parameters than training examples), and understanding why the training converges to solutions that generalize remains an open problem Zhang et al. [2017]. Several studies have highlighted the fact that the training procedure, i.e. mini-batch Stochastic Gradient Descent (SGD) leads to solutions that have specific properties in the loss landscape. However, even with plain Gradient Descent (GD) the solutions found in the over-parametrized regime are pretty good and this phenomenon is poorly understood. We propose an analysis of this behavior for feedforward networks with a ReLU activation function under the assumption of small initialization and learning rate and uncover a quantization effect: The weight vectors tend to concentrate at a small number of directions determined by the input data. As a consequence, we show that for given input data there are only finitely many, “simple” functions that can be obtained, independent of the network size. This puts these functions in analogy to linear interpolations (for given input data there are finitely many triangulations, which each determine a function by linear interpolation). We ask whether this analogy extends to the generalization properties while the usual distribution-independent generalization property does not hold, it could be that for e.g. smooth functions with bounded second derivative an approximation property holds which could “explain” generalization of networks (of unbounded size) to unseen inputs.
An Intrusion Detection System Based on KDD-99 Data using Data Mining Techniques and Feature Selection
(2014)

Internet and internet users are increasing day by day. Also due to rapid development of internet technology , security is becoming big issue. Intruders are monitoring comput er network continuously for attacks. A sophisticated firewall with efficient intrusion detection system (IDS) is required to pre vent computer network from attacks. A comprehensive study of litera tures proves that data mining techniques are more powerful techni que to develop IDS as a classifier. Performance of classif ier is a crucial issue in terms of its efficiency, also number of fe ature to be scanned by the IDS should also be optimized. In thi s paper two techniques C5.0 and artificial neural network (ANN) ar e utilized with feature selection. Feature selection techniques will discard some irrelevant features while C5.0 and ANN acts as a classifier to classify the data in either normal type or one of t he five types of attack.KDD99 data set is used to train and test the models ,C5.0 model with numbers of features is producing better r sults with all most 100% accuracy. Performances were also verified in terms of data partition size.
Reward, Motivation, and Reinforcement Learning
(2002)

There is substantial evidence that dopamine is involved in reward learning and appetitive conditioning. However, the major reinforcement learning-based theoretical models of classical conditioning (crudely, prediction learning) are actually based on rules designed to explain instrumental conditioning (action learning). Extensive anatomical, pharmacological, and psychological data, particularly concerning the impact of motivational manipulations, show that these models are unreasonable. We review the data and consider the involvement of a rich collection of different neural systems in various aspects of these forms of conditioning. Dopamine plays a pivotal, but complicated, role.
On the momentum term in gradient descent learning algorithms
(1999)

A momentum term is usually included in the simulations of connectionist learning algorithms. Although it is well known that such a term greatly improves the speed of learning, there have been few rigorous studies of its mechanisms. In this paper, I show that in the limit of continuous time, the momentum parameter is analogous to the mass of Newtonian particles that move through a viscous medium in a conservative force field. The behavior of the system near a local minimum is equivalent to a set of coupled and damped harmonic oscillators. The momentum term improves the speed of convergence by bringing some eigen components of the system closer to critical damping. Similar results can be obtained for the discrete time case used in computer simulations. In particular, I derive the bounds for convergence on learning-rate and momentum parameters, and demonstrate that the momentum term can increase the range of learning rate over which the system converges. The optimal condition for convergence is also analyzed.
Real time contextual collective anomaly detection over multiple data streams
(2014)

Anomaly detection has always been a critical and challenging problem in many application areas such as industry, healthcare, environment and finance. This problem becomes more di cult in the Big Data era as the data scale increases dramatically and the type of anomalies gets more complicated. In time sensitive applications like real time monitoring, data are often fed in streams and anomalies are required to be identified online across multiple streams with a short time delay. The new data characteristics and analysis requirements make existing solutions no longer suitable. In this paper, we propose a framework to discover a new type of anomaly called contextual collective anomaly over a collection of data streams in real time. A primary advantage of this solution is that it can be seamlessly integrated with real time monitoring systems to timely and accurately identify the anomalies. Also, the proposed framework is designed in a way with a low computational intensity, and is able to handle large scale data streams. To demonstrate the e↵ectiveness and e ciency of our framework, we empirically validate it on two real world applications.
Evaluating Induced CCG Parsers on Grounded Semantic Parsing
(2016)

We compare the effectiveness of four different syntactic CCG parsers for a semantic slotfilling task to explore how much syntactic supervision is required for downstream semantic analysis. This extrinsic, task-based evaluation provides a unique window to explore the strengths and weaknesses of semantics captured by unsupervised grammar induction systems. We release a new Freebase semantic parsing dataset called SPADES (Semantic PArsing of DEclarative Sentences) containing 93K cloze-style questions paired with answers. We evaluate all our models on this dataset. Our code and data are available at https://github.com/ sivareddyg/graph-parser.
Analysis of stairs-climbing ability for a tracked reconfigurable modular robot
(2005)

Stairs-climbing ability is the crucial performance of mobile robot for urban environment mission such as urban search and rescue or urban reconnaissance. The track type mobile mechanism has been widely applied for its advantages such as high stability, easy to control, low terrain pressure, and continuous drive. Stairs-climbing is a complicated process for a tracked mobile robot under kinematics and dynamics constraints. In this paper, the stairs-climbing process has been divided into riser climbing, riser crossing, and nose line climbing. During each climbing process, robot's mobility has been analyzed for its kinematics and dynamics factor. The track velocity and acceleration's influences on riser climbing have been analyzed. And the semiempirical design method of the track grouser and the module length has been provided in riser crossing and nose line climbing correspondingly. Finally, stairs-climbing experiments have been made on the two-module robot in line type, and three-module robot in line type and in triangle type respectively.
An Overview of the Theory and Applications of Metasurfaces: The Two-Dimensional Equivalents of Metamaterials
(2012)

Metamaterials are typically engineered by arranging a set of small scatterers or apertures in a regular array throughout a region of space, thus obtaining some desirable bulk electromagnetic behavior. The desired property is often one that is not normally found naturally (negative refractive index, near-zero index, etc.). Over the past ten years, metamaterials have moved from being simply a theoretical concept to a field with developed and marketed applications. Three-dimensional metamaterials can be extended by arranging electrically small scatterers or holes into a two-dimensional pattern at a surface or interface. This surface version of a metamaterial has been given the name metasurface (the term metafilm has also been employed for certain structures). For many applications, metasurfaces can be used in place of metamaterials. Metasurfaces have the advantage of taking up less physical space than do full three-dimensional metamaterial structures; consequently, metasurfaces offer the possibility of less-lossy structures. In this overview paper, we discuss the theoretical basis by which metasurfaces should be characterized, and discuss their various applications. We will see how metasurfaces are distinguished from conventional frequency-selective surfaces. Metasurfaces have a wide range of potential applications in electromagnetics (ranging from low microwave to optical frequencies), including: (1) controllable “smart” surfaces, (2) miniaturized cavity resonators, (3) novel wave-guiding structures, (4) angular-independent surfaces, (5) absorbers, (6) biomedical devices, (7) terahertz switches, and (8) fluid-tunable frequency-agile materials, to name only a few. In this review, we will see that the development in recent years of such materials and/or surfaces is bringing us closer to realizing the exciting speculations made over one hundred years ago by the work of Lamb, Schuster, and Pocklington, and later by Mandel'shtam and Veselago.
Millimeter-Wave Radar Sensor for Snow Height Measurements
(2017)

A small and lightweight frequency-modulated continuous-wave (FMCW) radar system is used for the determination of snow height by measuring the distance to the snow surface from a platform. The measurements have been performed at the Centre des Études de la Neige (Col de Porte), which is located near Grenoble in the French Alps. It is shown that the FMCW radar at millimeter-wave frequencies is an extremely promising approach for distance measurements to snow surfaces, e.g., in the mountains or in an Arctic environment. The characteristics of the radar sensor are described in detail. The relevant accuracy to measure the distance to a snow layer is shown at different heights and over an extended time duration. A dedicated laser snow telemeter is used as reference. In addition, the reflection from different types of snow is shown.
Real-time multi-feature based fire flame detection in video
(2017)

In this paper, we present a new approach to detect fire flame by processing and analyzing the stationary camera videos. For a fire detection system, it is desired to be sensitive and reliable. The proposed method improves not only the sensitivity but also the reliability through reducing the susceptibility to false alarms. The proposed approach based on multi-feature, i.e., chromatic features, dynamic features, texture features, and contour features, can both improve the sensitivity and reliability in fire detection. In our approach, we adopt a novel algorithm to extract the moving region and analyze the frequency of flickers. Experimental results show that the proposed method can run in real-time and performs favorably against the state-of-art methods with higher Page 1 of 22 IET Review Copy Only IET Image Processing This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication in an issue of the journal. To cite the paper please use the doi provided on the Digital Library page.
Melody Generation for Pop Music via Word Representation of Musical Properties
(2018)

Automatic melody generation for pop music has been a long-time aspiration for both AI researchers and musicians. However, learning to generate euphonious melody has turned out to be highly challenging due to a number of factors. Representation of multivariate property of notes has been one of the primary challenges. It is also difficult to remain in the permissible spectrum of musical variety, outside of which would be perceived as a plain random play without auditory pleasantness. Observing the conventional structure of pop music poses further challenges. In this paper, we propose to represent each note and its properties as a unique ‘word,’ thus lessening the prospect of misalignments between the properties, as well as reducing the complexity of learning. We also enforce regularization policies on the range of notes, thus encouraging the generated melody to stay close to what humans would find easy to follow. Furthermore, we generate melody conditioned on song part information, thus replicating the overall structure of a full song. Experimental results demonstrate that our model can generate auditorily pleasant songs that are more indistinguishable from human-written ones than previous models. 1
PinMe: Tracking a Smartphone User around the World
(2018)

With the pervasive use of smartphones that sense, collect, and process valuable information about the environment, ensuring location privacy has become one of the most important concerns in the modern age. A few recent research studies discuss the feasibility of processing sensory data gathered by a smartphone to locate the phone’s owner, even when the user does not intend to share his location information, e.g., when the user has turned off the Global Positioning System (GPS) on the device. Previous research efforts rely on at least one of the two following fundamental requirements, which impose significant limitations on the adversary: (i) the attacker must accurately know either the user’s initial location or the set of routes through which the user travels and/or (ii) the attacker must measure a set of features, e.g., device acceleration, for different potential routes in advance and construct a training dataset. In this paper, we demonstrate that neither of the above-mentioned requirements is essential for compromising the user’s location privacy. We describe PinMe, a novel user-location mechanism that exploits non-sensory/sensory data stored on the smartphone, e.g., the environment’s air pressure and device’s timezone, along with publicly-available auxiliary information, e.g., elevation maps, to estimate the user’s location when all location services, e.g., GPS, are turned off. Unlike previously-proposed attacks, PinMe neither requires any prior knowledge about the user nor a training dataset on specific routes. We demonstrate that PinMe can accurately estimate the user’s location during four activities (walking, traveling on a train, driving, and traveling on a plane). We also suggest several defenses against the proposed attack.
A Taxi Order Dispatch Model based On Combinatorial Optimization
(2017)

Taxi-booking apps have been very popular all over the world as they provide convenience such as fast response time to the users. The key component of a taxi-booking app is the dispatch system which aims to provide optimal matches between drivers and riders. Traditional dispatch systems sequentially dispatch taxis to riders and aim to maximize the driver acceptance rate for each individual order. However, the traditional systems may lead to a low global success rate, which degrades the rider experience when using the app. In this paper, we propose a novel system that attempts to optimally dispatch taxis to serve multiple bookings. The proposed system aims to maximize the global success rate, thus it optimizes the overall travel efficiency, leading to enhanced user experience. To further enhance users' experience, we also propose a method to predict destinations of a user once the taxi-booking APP is started. The proposed method employs the Bayesian framework to model the distribution of a user's destination based on his/her travel histories.
 We use rigorous A/B tests to compare our new taxi dispatch method with state-of-the-art models using data collected in Beijing. Experimental results show that the proposed method is significantly better than other state-of-the art models in terms of global success rate (increased from 80% to 84%). Moreover, we have also achieved significant improvement on other metrics such as user's waiting-time and pick-up distance. For our destination prediction algorithm, we show that our proposed model is superior to the baseline model by improving the top-3 accuracy from 89% to 93%. The proposed taxi dispatch and destination prediction algorithms are both deployed in our online systems and serve tens of millions of users everyday.
LiveNet: Improving features generalization for face liveness detection using convolution neural networks
(2018)

Performance of face liveness detection algorithms in cross-database face liveness detection tests is one of the key issues in face-biometric based systems. Recently, Convolution Neural Networks (CNN) classifiers have shown remarkable performance in intra-database face liveness detection tests. However, a little effort has been made to improve the generalization capability of CNN classifiers for cross-database and unconstrained face liveness detection tests. In this paper, we propose an efficient strategy for training deep CNN classifiers for face liveness detection task. We utilize continuous data-randomization (like bootstrapping) in the form of small mini-batches during training CNN classifiers on small scale face antispoofing database. Experimental results revealed that the proposed approach reduces the training time by 18.39%, while significantly lowering the HTER by 8.28% and 14.14% in cross-database tests on CASIAFASD and Replay-Attack database respectively as compared to state-of-the-art approaches. Additionally, the proposed approach achieves satisfactory results on intra-database and cross-database face liveness detection tests, claiming a good generality over other state-of-the-art face anti-spoofing approaches. © 2018 Elsevier Ltd. All rights reserved.
Character Recognition Using Convolutional Neural Networks
(2006)

Pattern recognition is one of the traditional uses of neural networks. When trained with gradient-based learning methods, these networks can learn the classification of input data by example. An introduction to classifiers and gradient-based learning is given. It is shown how several perceptrons can be combined and trained gradient-based. Furthermore, an overview of convolutional neural networks, as well as a real-world example, are discussed.
Inverted Files Versus Signature Files for Text Indexing
(1998)

Two well-known indexing methods are inverted files and signature files. We have undertaken a detailed comparison of these two approaches in the context of text indexing, paying particular attention to query evaluation speed and space requirements. We have examined their relative performance using both experimentation and a refined approach to modeling of signature files, and demonstrate that inverted files are distinctly superior to signature files. Not only can inverted files be used to evaluate typical queries in less time than can signature files, but inverted files require less space and provide greater functionality. Our results also show that a synthetic text database can provide a realistic indication of the behavior of an actual text database. The tools used to generate the synthetic database have been made publicly available
Color nonuniformity in projection-based displays: analysis and solutions
(2004)

Large-area displays made up of several projectors show significant variation in color. Here, we identify different projector parameters that cause the color variation and study their effects on the luminance and chrominance characteristics of the display. This work leads to the realization that luminance varies significantly within and across projectors, while chrominance variation is relatively small, especially across projectors of same model. To address this situation, we present a method to achieve luminance matching across all pixels of a multiprojector display that results in photometrically uniform displays. We use a camera as a measurement device for this purpose. Our method comprises a one-time calibration step that generates a per channel per projector luminance attenuation map (LAM), which is then used to correct any image projected on the display at interactive rates on commodity graphics hardware. To the best of our knowledge, this is the first effort to match luminance across all the pixels of a multiprojector display.
Entity Synonyms for Structured Web Search
(2012)

Nowadays, there are many queries issued to search engines targeting at finding values from structured data (e.g., movie showtime of a specific location). In such scenarios, there is often a mismatch between the values of structured data (how content creators describe entities) and the web queries (how different users try to retrieve them). Therefore, recognizing the alternative ways people use to reference an entity, is crucial for structured web search. In this paper, we study the problem of automatic generation of entity synonyms over structured data toward closing the gap between users and structured data. We propose an offline, data-driven approach that mines query logs for instances where content creators and web users apply a variety of strings to refer to the same webpages. This way, given a set of strings that reference entities, we generate an expanded set of equivalent strings (entity synonyms) for each entity. Our framework consists of three modules: candidate generation, candidate selection, and noise cleaning. We further study the cause of the problem through the identification of different entity synonym classes. The proposed method is verified with experiments on real-life data sets showing that we can significantly increase the coverage of structured web queries with good precision.